import argparse
import logging
import os

import datasets
import tensorflow as tf

from transformers import AutoTokenizer


_SCREAMING_SNAKE_CASE 		=      logging.getLogger(__name__)





def 			lowercase(	)		->	int:







 '''simple docstring'''
 UpperCamelCase			  =      argparse.ArgumentParser(
     description="""Prepare TFRecord shards from pre-tokenized samples of the wikitext dataset."""       )
 parser.add_argument(
     """--dataset_name"""							,					type=UpperCamelCase_							,					default="""wikitext"""							,					help="""Name of the training. Explore datasets at: hf.co/datasets."""							,					)
 parser.add_argument(
     """--dataset_config"""							,					type=UpperCamelCase_							,					default="""wikitext-103-raw-v1"""							,					help="""Configuration name of the dataset."""       )
 parser.add_argument(
     """--tokenizer_name_or_path"""							,					type=UpperCamelCase_							,					default="""sayakpaul/unigram-tokenizer-wikitext"""							,					help="""Tokenizer identifier. Can be a local filepath or a Hub identifier."""							,					)
 parser.add_argument(
     """--shard_size"""							,					type=UpperCamelCase_							,					default=1000							,					help="""Number of entries to go in a single shard."""							,					)
 parser.add_argument("""--split"""							,					type=UpperCamelCase_							,					default="""train"""							,					choices=["""train""", """test""", """validation"""]       )
 parser.add_argument(
     """--limit"""							,					default=UpperCamelCase_							,					type=UpperCamelCase_							,					help="""Limit the number of shards (used for debugging)."""							,					)
 parser.add_argument(
     """--max_length"""							,					type=UpperCamelCase_							,					default=512							,					help="""Maximum sequence length. For training on TPUs, it helps to have a maximum"""
     """ sequence length that is a multiple of 8."""							,					)
 parser.add_argument(
     """--output_dir"""							,					default="""tf-tpu"""							,					type=UpperCamelCase_							,					help="""Output directory where the TFRecord shards will be saved. If the"""
     """ path is appended with `gs://` ('gs://tf-tpu', for example) then the TFRecord"""
     """ shards will be directly saved to a Google Cloud Storage bucket."""							,					)

 UpperCamelCase			  =      parser.parse_args()
 return args





def 			lowercase(	UpperCamelCase_       )		->	Tuple:







 '''simple docstring'''
 def fn(UpperCamelCase_       ):
  return tokenizer(examples["""text"""]       )

 return fn





def 			lowercase(	UpperCamelCase_       )		->	int:







 '''simple docstring'''
 UpperCamelCase			  =      []
 for i in range(len(tokenized_data["""input_ids"""]       )       ):
  UpperCamelCase			  =      {
      """input_ids""": tf.train.Feature(intaa_list=tf.train.IntaaList(value=tokenized_data["""input_ids"""][i]       )       ),
      """attention_mask""": tf.train.Feature(
          intaa_list=tf.train.IntaaList(value=tokenized_data["""attention_mask"""][i]       )       ),
  }
  UpperCamelCase			  =      tf.train.Features(feature=UpperCamelCase_       )
  UpperCamelCase			  =      tf.train.Example(features=UpperCamelCase_       )
  UpperCamelCase			  =      example.SerializeToString()
  records.append(UpperCamelCase_       )
 return records





def 			lowercase(	UpperCamelCase_       )		->	Dict:







 '''simple docstring'''
 UpperCamelCase			  =      datasets.load_dataset(args.dataset_name							,					args.dataset_config							,					split=args.split       )

 if args.limit is not None:
  UpperCamelCase			  =      min(len(UpperCamelCase_       )							,					args.limit       )
  UpperCamelCase			  =      dataset.select(range(UpperCamelCase_       )       )
  print(f"""Limiting the dataset to {args.limit} entries."""       )

 UpperCamelCase			  =      AutoTokenizer.from_pretrained(args.tokenizer_name_or_path       )

 # Handle output directory creation.
 # For serializing into a Google Cloud Storage Bucket, one needs to first
 # create a bucket.
 if "gs" not in args.output_dir:
  if not os.path.exists(args.output_dir       ):
   os.makedirs(args.output_dir       )
  UpperCamelCase			  =      os.path.join(args.output_dir							,					args.split       )
  if not os.path.exists(UpperCamelCase_       ):
   os.makedirs(UpperCamelCase_       )
 else:
  UpperCamelCase			  =      os.path.join(args.output_dir							,					args.split       )

 # Tokenize the whole dataset at once.
 UpperCamelCase			  =      tokenize_function(UpperCamelCase_       )
 UpperCamelCase			  =      dataset.map(UpperCamelCase_							,					batched=UpperCamelCase_							,					num_proc=4							,					remove_columns=["""text"""]       )

 # We need to concatenate all our texts together, and then split the result
 # into chunks of a fixed size, which we will call block_size. To do this, we
 # will use the map method again, with the option batched=True. When we use batched=True,
 # the function we pass to map() will be passed multiple inputs at once, allowing us
 # to group them into more or fewer examples than we had in the input.
 # This allows us to create our new fixed-length samples. The advantage of this
 # method is that we don't lose a whole lot of content from the dataset compared to the
 # case where we simply tokenize with a pre-defined max_length.

 def group_texts(UpperCamelCase_       ):
  # Concatenate all texts.
  UpperCamelCase			  =      {k: sum(examples[k]							,					[]       ) for k in examples.keys()}
  UpperCamelCase			  =      len(concatenated_examples[list(examples.keys()       )[0]]       )
  # We drop the small remainder, though you could add padding instead if the model supports it
  # In this, as in all things, we advise you to follow your heart 🫀
  UpperCamelCase			  =      (total_length // args.max_length) * args.max_length
  # Split by chunks of max_len.
  UpperCamelCase			  =      {
      k: [t[i : i + args.max_length] for i in range(0							,					UpperCamelCase_							,					args.max_length       )]
      for k, t in concatenated_examples.items()
  }
  return result

 UpperCamelCase			  =      dataset_tokenized.map(UpperCamelCase_							,					batched=UpperCamelCase_							,					batch_size=1000							,					num_proc=4       )

 UpperCamelCase			  =      0
 UpperCamelCase			  =      0
 for shard in range(0							,					len(UpperCamelCase_       )							,					args.shard_size       ):
  UpperCamelCase			  =      grouped_dataset[shard : shard + args.shard_size]
  UpperCamelCase			  =      len(dataset_snapshot["""input_ids"""]       )
  UpperCamelCase			  =      os.path.join(UpperCamelCase_							,					f"""dataset-{shard_count}-{records_containing}.tfrecord"""       )
  UpperCamelCase			  =      get_serialized_examples(UpperCamelCase_       )

  with tf.io.TFRecordWriter(UpperCamelCase_       ) as out_file:
   for i in range(len(UpperCamelCase_       )       ):
    UpperCamelCase			  =      serialized_examples[i]
    out_file.write(UpperCamelCase_       )
   print("""Wrote file {} containing {} records""".format(UpperCamelCase_							,					UpperCamelCase_       )       )

  shard_count += 1
  total_records += records_containing

 with open(f"""split-{args.split}-records-count.txt"""							,					"""w"""       ) as f:
  print(f"""Total {args.split} records: {total_records}"""							,					file=UpperCamelCase_       )


if __name__ == "__main__":
     _SCREAMING_SNAKE_CASE 		=      parse_args()
     main(args)

def 			lowercase(	UpperCamelCase_							,					UpperCamelCase_       )		->	List[str]:







 '''simple docstring'''
 _enforce_args(UpperCamelCase_							,					UpperCamelCase_       )
 if n == 0:
  return 0
 UpperCamelCase			  =      float("""-inf"""       )
 for i in range(1							,					n + 1       ):
  UpperCamelCase			  =      max(
      UpperCamelCase_							,					prices[i - 1] + naive_cut_rod_recursive(n - i							,					UpperCamelCase_       )       )

 return max_revue





def 			lowercase(	UpperCamelCase_							,					UpperCamelCase_       )		->	Optional[Any]:







 '''simple docstring'''
 _enforce_args(UpperCamelCase_							,					UpperCamelCase_       )
 UpperCamelCase			  =      [float("""-inf"""       ) for _ in range(n + 1       )]
 return _top_down_cut_rod_recursive(UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_       )





def 			lowercase(	UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_       )		->	List[Any]:







 '''simple docstring'''
 if max_rev[n] >= 0:
  return max_rev[n]
 elif n == 0:
  return 0
 else:
  UpperCamelCase			  =      float("""-inf"""       )
  for i in range(1							,					n + 1       ):
   UpperCamelCase			  =      max(
       UpperCamelCase_							,					prices[i - 1] + _top_down_cut_rod_recursive(n - i							,					UpperCamelCase_							,					UpperCamelCase_       )							,					)

  UpperCamelCase			  =      max_revenue

 return max_rev[n]





def 			lowercase(	UpperCamelCase_							,					UpperCamelCase_       )		->	Union[str, Any]:







 '''simple docstring'''
 _enforce_args(UpperCamelCase_							,					UpperCamelCase_       )

 # length(max_rev) = n + 1, to accommodate for the revenue obtainable from a rod of
 # length 0.
 UpperCamelCase			  =      [float("""-inf"""       ) for _ in range(n + 1       )]
 UpperCamelCase			  =      0

 for i in range(1							,					n + 1       ):
  UpperCamelCase			  =      max_rev[i]
  for j in range(1							,					i + 1       ):
   UpperCamelCase			  =      max(UpperCamelCase_							,					prices[j - 1] + max_rev[i - j]       )

  UpperCamelCase			  =      max_revenue_i

 return max_rev[n]





def 			lowercase(	UpperCamelCase_							,					UpperCamelCase_       )		->	Union[str, Any]:







 '''simple docstring'''
 if n < 0:
  UpperCamelCase			  =      f"""n must be greater than or equal to 0. Got n = {n}"""
  raise ValueError(UpperCamelCase_       )

 if n > len(UpperCamelCase_       ):
  UpperCamelCase			  =      (
      """Each integral piece of rod must have a corresponding price. """
      f"""Got n = {n} but length of prices = {len(UpperCamelCase_       )}"""
  )
  raise ValueError(UpperCamelCase_       )





def 			lowercase(	)		->	str:







 '''simple docstring'''
 UpperCamelCase			  =      [6, 10, 12, 15, 20, 23]
 UpperCamelCase			  =      len(UpperCamelCase_       )

 # the best revenue comes from cutting the rod into 6 pieces, each
 # of length 1 resulting in a revenue of 6 * 6 = 36.
 UpperCamelCase			  =      36

 UpperCamelCase			  =      top_down_cut_rod(UpperCamelCase_							,					UpperCamelCase_       )
 UpperCamelCase			  =      bottom_up_cut_rod(UpperCamelCase_							,					UpperCamelCase_       )
 UpperCamelCase			  =      naive_cut_rod_recursive(UpperCamelCase_							,					UpperCamelCase_       )

 assert expected_max_revenue == max_rev_top_down
 assert max_rev_top_down == max_rev_bottom_up
 assert max_rev_bottom_up == max_rev_naive


if __name__ == "__main__":
     main()
